Feedforward carrier recovery via pilot-aided transmission for single-carrier systems with arbitrary M-QAM constellations
Why this work is in the frame
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Bibliographic record
Abstract
We exploit pilot-aided (PA) transmission enabled by single-sideband (SSB) subcarrier modulation of both quadrature signals in the DSP domain to achieve fully feedforward carrier recovery (FFCR) in single-carrier (SC) coherent systems with arbitrary M-QAM constellations. A thorough mathematical description of the proposed PA-FFCR is presented, its linewidth tolerance is assessed by simulations and compared to other FFCR schemes in literature. Also, implementation and complexity issues of PA-FFCR are presented and briefly compared with other CR schemes. Simulation results show that PA-FFCR performs close to the best known CR technique in the literature with less computation complexity. Quantitatively, for 1 dB optical-signal-to-noise-ratio (OSNR) penalty at BER = 3.8 × 10(-3), PA-FFCR tolerates linewidth-symbol-duration products (Δf.Ts) of 1.5 × 10(-4) (4-QAM), 4 × 10(-5) (16-QAM) and 1 × 10(-5) (64-QAM). Finally, we propose the use of maximum likelihood (ML) phase estimation next to pilot phase compensation. This significantly improves tolerable Δf.Ts values to 7.5 × 10(-4) (4-QAM), 1.8 × 10(-4) (16-QAM) and 3.5 × 10(-5) (64-QAM). It turns out that PA-FFCR with ML always performs better or at least the same compared to other CR techniques known in literature with lower complexity in addition to the fact that pilot information can be as well exploited for tasks other than CR e.g., fiber nonlinearity compensation, with no extra complexity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it